A tensor-channel equivariant GNN based on PaiNN propagates symmetric rank-2 tensor features during message passing and achieves lower full-tensor and anisotropic error than readout-only and MACE baselines on QM7-X geometries.
Mace-polar-1: A polarisable electrostatic foun- dation model for molecular chemistry
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Polarizable atomic multipoles predicted locally plus linear response for non-local effects improve electrostatic accuracy in MLIPs and recover Born effective charges, polarizabilities, and experimental infrared spectra.
mlip v2 is a new software release that integrates API redesign, e3j backend, eSEN model, improved charge modeling, and expanded simulation capabilities to support larger-scale molecular modeling.
Differentiable hybrid force fields combine physical models with neural corrections to enable fast, accurate, and calibratable simulations for scalable autonomous electrolyte discovery.
citing papers explorer
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Tensor Channel Equivariant Graph Neural Networks for Molecular Polarizability Prediction
A tensor-channel equivariant GNN based on PaiNN propagates symmetric rank-2 tensor features during message passing and achieves lower full-tensor and anisotropic error than readout-only and MACE baselines on QM7-X geometries.
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Polarizable atomic multipoles for learning long-range electrostatics
Polarizable atomic multipoles predicted locally plus linear response for non-local effects improve electrostatic accuracy in MLIPs and recover Born effective charges, polarizabilities, and experimental infrared spectra.
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Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
mlip v2 is a new software release that integrates API redesign, e3j backend, eSEN model, improved charge modeling, and expanded simulation capabilities to support larger-scale molecular modeling.
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Differentiable hybrid force fields support scalable autonomous electrolyte discovery
Differentiable hybrid force fields combine physical models with neural corrections to enable fast, accurate, and calibratable simulations for scalable autonomous electrolyte discovery.